Development and Evaluation of Personalized Explainable Machine Learning Models to Predict and Prevent Nocturnal Hypoglycemia in Type 1 Diabetes

开发和评估个性化可解释机器学习模型以预测和预防 1 型糖尿病夜间低血糖

• 批准号: 10373516
• 负责人: Clara Marcela Mosquera-Lopez
• 金额: $ 16.26万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10373516
• 关键词: Accounting; Adult; Affect; Algorithms; Beds; Big Data; Carbohydrates; Cellular Phone; Cessation of life; Clinic; Clinical Research; Consumption; Cross-Over Studies; Dangerousness; Data; Data Set; Detection; Development; Emergency Care; Engineering; Evaluation; Event; Exercise; Fiber; Fright; Glucose; Human; Hyperglycemia; Hypoglycemia; Individual; Infusion Pumps; Injection of therapeutic agent; Injections; Injury; Insulin; Insulin-Dependent Diabetes Mellitus; Intervention; Learning; Machine Learning; Macronutrients Nutrition; Measurement; Measures; Modeling; Nutrient; Outcome Measure; Participant; Phase; Physical activity; Psychological Transfer; Pump; Randomized; Registries; Risk; Running; Seizures; Sensitivity and Specificity; Sleep; Specificity; Symptoms; Syndrome; System; Testing; Time; Training; Unconscious State; Update; Wireless Technology; base; blood glucose regulation; cohort; comparison intervention; data management; design; diabetes management; diabetes mellitus therapy; experience; glycemic control; high risk; hypoglycemia unawareness; improved; intervention effect; large datasets; personalized decision; poor sleep; population based; prediction algorithm; predictive modeling; prevent; primary outcome; recruit; secondary outcome; sensor; sensor technology; side effect; simulation; sleep quality; subcutaneous; support tools

Development and Evaluation of Personalized Explainable Machine Learning Models to Predict and Prevent Nocturnal Hypoglycemia in Type 1 Diabetes Project Summary Hypoglycemia (glucose < 70 mg/dL) remains the limiting factor for achieving optimal glycemic control in type 1 diabetes (T1D), with nocturnal hypoglycemia being particularly dangerous. Nocturnal hypoglycemia may result in physical injury, poor sleep quality, fear of hypoglycemia, and hypoglycemia unawareness. Severe episodes can cause seizures and unconsciousness requiring emergency care, and even death (dead in bed syndrome). While automated insulin delivery (AID) systems have shown benefits in glucose control during the night, nighttime hypoglycemia still occurs. Moreover, many people with T1D manage their glucose with continuous subcutaneous infusion pump (CSII) therapy or multiple daily insulin injections (MDI) therapy. Data updated between 2013 and 2014 from 16,061 individuals with T1D participating in the T1D Exchange clinic registry showed that approximately 40% participants managed their glucose with MDI. In this project, we propose to develop and evaluate a personalized decision support tool that collects and analyzes glucose measurements, insulin, meals, and physical activity data to predict at bedtime the likelihood of overnight hypoglycemia and recommend a proactive carbohydrate intervention to substantially reduce nocturnal hypoglycemia. In the engineering development phase of the project, we will use unique datasets of time-matched glucose management data (i.e., continuous glucose measurements, insulin, meals, and exercise) from pump, closed- loop and MDI users to extract information about the major contributors to nocturnal hypoglycemia risk and train a population-based prediction model that will be personalized over time to better capture inter-subject variability. We will design a bedtime intervention consisting of a bedtime smart snack with variable nutrient content that can prevent nighttime hypoglycemia. Snacks will vary by macronutrient content and size to optimize time to peak post-prandial glycemia that will match the timing to predicted episode of hypoglycemia. We will conduct a randomized cross-over study to evaluate our smartphone-based decision support tool on a cohort of 20 people with T1D who are MDI users and are at higher risk of experiencing hypoglycemia. Participants will be randomly assigned to either first use CGM only (control period) followed by a smartphone- based decision support tool + nocturnal hypoglycemia intervention (intervention period), or vice-versa. The control and intervention periods will have a duration of three weeks each. We will measure the effect of the intervention by comparing the percent time in nocturnal hypoglycemia during the control period vs. the intervention period. We will also retrospectively measure the accuracy of the prediction model in predicting nocturnal hypoglycemia using data from the control period. We expect that the proposed bedtime intervention will lead to a significant reduction in time spent in hypoglycemia overnight of at least 50% reduction relative to baseline.

开发和评估个性化的可解释的机器学习模型，以预测和 预防1型糖尿病中的夜间低血糖 项目摘要 低血糖（葡萄糖<70 mg/dl）仍然是实现1型最佳血糖控制的限制因素 糖尿病（T1D），夜间低血糖特别危险。夜间低血糖可能会导致 在身体伤害，睡眠质量差，对低血糖症的恐惧和低血糖不认识。严重的发作 可能引起需要急诊护理甚至死亡（床综合症死亡）的癫痫发作和无意识。 虽然自动胰岛素输送（AID）系统在夜间显示出葡萄糖控制的好处，但 夜间低血糖仍会发生。此外，许多患有T1D的人连续地管理葡萄糖 皮下输注泵（CSII）疗法或多次每日胰岛素注射（MDI）治疗。数据更新 在2013年至2014年之间，有16,061名T1D参加T1D Exchange Clinic注册表的人 表明大约40％的参与者用MDI管理了葡萄糖。在这个项目中，我们建议 开发和评估一个个性化的决策支持工具，该工具收集和分析葡萄糖测量， 胰岛素，餐和体育活动数据可在睡前预测过夜低血糖和 建议主动碳水化合物干预措施，以大大减少夜间低血糖症。在 该项目的工程开发阶段，我们将使用时间匹配的葡萄糖的独特数据集 泵的管理数据（即连续葡萄糖测量，胰岛素，餐和运动） 循环和MDI用户提取有关夜间低血糖风险和训练的主要贡献者的信息 一个基于人群的预测模型，随着时间的推移将个性化以更好地捕获主体间 可变性。我们将设计一个睡前干预措施，该干预措施包括带有可变营养的就寝时间智能小吃 可以防止夜间低血糖的内容。小吃会因大量营养素的含量和大小而异 优化时间峰值后帕克血症的时间，这将与预测的低血糖发作相匹配。 我们将进行一项随机交叉研究，以评估我们基于智能手机的决策支持工具 有20名T1D的人群是MDI使用者，并且患有低血糖的风险更高。 参与者将被随机分配到首先使用CGM（控制期），然后是智能手机 - 基于决策支持工具 +夜间低血糖干预（干预期）或反之亦然。这 控制和干预期的持续时间为三个星期。我们将衡量 通过比较夜间低血糖的时间百分比在控制期间的干预措施与 干预期。我们还将回顾性测量预测模型的准确性 夜间低血糖使用控制期的数据。我们希望拟议的就寝时间干预 一夜之间，在低血糖中花费的时间大幅减少了至少50％ 基线。